[1]} give a causal explanation to diversity and correlation shifts and proceed to benchmark out-of-distribution methods, showing that the aforementioned shifts are the main components of the distribution shifts found in OOD datasets. Similarly [2]}, introduce an influence function and a novel metric to evaluate OOD while analyzing their contributions from a causal standpoint.
[3]} propose a causal semantic generative model in order to address OOD prediction from a single training domain. Utilizing the causal invariance principle they disentangle the semantic causes of prediction and other variation factors achieving impressive results.
